Abstract Post-translational modifications (PTMs) are under significant focus in molecular biomedicine due to their importance in signal transduction in most cellular and organismal processes. Characterization of PTMs, discrimination between functional and inert PTMs, quantification of their occupancies and PTM crosstalk are demanding tasks in each biosystem. On top of that, the study of each PTM often necessitates a particular laborious experimental design. Here, we present a PTM-centric proteome informatic pipeline for prediction of relevant PTMs in mass spectrometry-based proteomics data in the absence of a priori information. Upon prediction, such PTMs can be incorporated in a refined database search. As a practical application, we showed how this pipeline suggested performing glycoproteomics in oral squamous cell carcinoma based on proteome profile of primary tumors. Subsequently, using proteome profiling of treated cells with two PTM-modulating kinase inhibitors, we experimentally identified cellular proteins that are differentially expressed in response to multikinase inhibitors dasatinib and staurosporine. Computational enrichment analysis was employed to determine the potential PTMs of protein targets for both drugs. Finally, we conducted an additional round of database search with the predicted PTMs. Our pipeline helped to analyze the enriched PTMs and even the detected proteins that were not identified in the initial search. Our findings support the idea of PTM-centric searching of MS data in proteomics based on computational enrichment analysis and we propose that this approach be integrated into future proteomics search engines.

Monitoring Functional Post-Translational Modifications Using a Data-Driven Proteome Informatic Pipeline Based on PEIMAN2